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In recent years, various biodiesels have been developed to decrease pollutant emissions from compression ignition engine. However, the current research focuses on reducing the pollutant components without considering the mechanical vibration that occurred due to the changes in fuel properties such as viscosity, calorific values, density, and bulk modulus. It is important to explore the relationships between fuel properties and engine vibration. Mechanical vibration could cause power loss and affect the lifetime of the engine. In this investigation, a lister-pitter 3-cylinder diesel engine was used to analyse the mechanical vibration of three different fuels including diesel, waste cooking oil biodiesel (WCOB), and lamb fat biodiesel (LFB). The high-frequency vibration sensors were mounted on the cylinder head to monitor and assess the vibration performance.

Accurate surrounding vehicle motion prediction is critical for enabling safe, high quality autonomous driving decision-making and motion planning. Aiming at the problem that the current deep learning-based trajectory prediction methods are not accurate and effective for extracting the interaction between vehicles and the road environment information, we design a target vehicle intention-aware dual attention network (IDAN), which establishes a multi-task learning framework combining intention network and trajectory prediction network, imposing dual constraints. The intention network generates an intention encoding representing the driver’s intention information. It inputs it into the attention module of the trajectory prediction network to assist the trajectory prediction network to achieve better prediction accuracy.

With the improvement of sensor accuracy, sensor data plays an increasingly important role in intelligent vehicle motion control. Good use of sensor data can improve the control of vehicles. However, data-based end-to-end control has the disadvantages of poorly interpreted control models and high time costs; model-based control methods often have difficulties designing high-fidelity vehicle controllers because of model errors and uncertainties in building vehicle dynamics models. In the face of high-speed steering conditions, vehicle control is difficult to ensure stability and safety. Therefore, this paper proposes a hybrid model and data-driven control method. Based on the vehicle state data and road information data provided by vehicle sensors, the method constructs a deep neural network based on LSTM and Attention, which is used as a compensator to solve the performance degradation of the LQR controller due to modeling errors.

When heated fuel is injected into an ambient environment below its saturation pressure, the fuel could reach superheated state and experience flash boiling. Comparing with the non-flash boiling spray, namely the single phase liquid spray, flash boiling spray is characterized by its nature of two phase flow, due to vapor bubbles constantly generating inside the liquid phase. The behavior of those microscopic scale bubbles could introduce prompt spray atomization and vaporization, resulting in dramatically different spray characteristics. Comparing with the sprays generated via a high pressure injection system, the flash boiling spray has much shorter penetration, wider spray angle, more uniformly distributed mass, quicker evaporation, and smaller drop sizes, etc., which are ideal for the direct-injection (DI) gasoline and diesel engine applications without the hassle and the high cost associated with the high pressure injection system.

Superheated spray is expected to improve the fuel atomization and evaporation processes by introducing fuel temperature as a new control parameter in spark-ignited direct-injection (SIDI) engines. In this study, flow fields of n-hexane spray from a multi-hole injector in both vertical and cross-sectional directions were investigated by using high-speed particle image velocimetry (HS-PIV) within the lower density regions. The results provide insight to the spray-collapsing processes under various superheated conditions. It was found that in axial direction, the vertical velocity increases while the radial velocity decreases with increasing superheat degree, which determines the convergent spray structure. In cross-sectional direction, the dynamic variation of the spray structure and interaction among spray plumes were investigated. The relationship between the spray structure and flow field was found. The flow patterns during and after the injection are significantly different.

Spray atomization and evaporation are expected to be improved by injecting fuel at a superheated state. However, the breakup mechanism and evaporation processes of superheated sprays have not been clarified. In previous studies [1], the multi-hole spray flow-field on the vertical plane through the spray axis was investigated by using high-speed particle image velocimetry (PIV). The results showed that the spray plumes collapse to the spray axis under high superheat conditions. It's also proven that the superheat degree is the predominant factor influencing the structure and the flow-field of the spray. To further understand this process, the interaction among spray plumes on three cross-sectional planes under various superheated conditions is investigated. In this study, n-hexane sprays generated from an eight-hole DI injector were measured using a high-speed PIV system. The results provide insight to the spray-collapse processes and the interaction between the spray plumes.

A laser sheet imaging system with Mie-scattering and Laser Induced Fluorescence (LIF) was used to investigate the spray characteristics of gasoline, methanol and ethanol fuels. A range of conditions found in today's gasoline engines were investigated including that observed during engine cold-start. Both a swirl injector and a multi-hole fuel injector were examined for each of the three fuels. A combination of the second harmonic (532 nm) and the fourth harmonic (266 nm) was generated simultaneously using a Nd:YAG laser system to illuminate the spray. The Mie-scattering technique was used to characterize the liquid phase of the spray while the LIF technique was used to detect a combination of liquid and vapor phases. While gasoline naturally fluoresced, the dopant TEA was added to the methanol and ethanol fuels as a fuel tracer. The Mie-scattering and LIF signals were captured simultaneously using a CCD camera along with an image doubler.

This paper reports tests to determine the effect of R- l34a-oil mixtures on condensation in a 1.32 mm hydraulic diameter extruded aluminum tube used in automotive refrigerant condensers. The tests were performed at 8 kW/mz heat flux for 600, 1000, and 1800 kg/m2s mass velocity with 3.25,6, and 10% oil concentration (by mass). At low vapor qualities, oil increases the condensation coefficient (h), and at vapor qualities above 50%, oil reduces the h-value. The h-reduction is more severe at increased oil content. Oil increases the pressure gradient, and the pressure-gradient increase is more severe at higher oil concentration.